CoAMD unifies skeleton-based action recognition and text-to-motion generation through autoregressive diffusion guided by a multi-modal recognizer, reporting SOTA results on 13 benchmarks for four tasks.
Photorealistic text-to-image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022
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AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.
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Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition
CoAMD unifies skeleton-based action recognition and text-to-motion generation through autoregressive diffusion guided by a multi-modal recognizer, reporting SOTA results on 13 benchmarks for four tasks.
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Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?
AdaScope adaptively selects optimal RL intervention points during diffusion denoising by monitoring structural and semantic changes, delivering 66% higher performance at 59% lower cost than full-trajectory RL baselines.